Bayesian Spatial Modelling withR-INLA

Bayesian Spatial Modelling with R-INLA

The principles behind the interface to continuous domain spatial models in the RINLA software package for R are described. The Integrated Nested Laplace Approximation (INLA) approach proposed by Rue, Martino, and Chopin (2009) is a computationally effective alternative to MCMC for Bayesian inference. INLA is designed for latent Gaussian models, a very wide and flexible class of models ranging f...

Bayesian computing with INLA: New features

The INLA approach for approximate Bayesian inference for latent Gaussian models has been shown to give fast and accurate estimates of posterior marginals and also to be a valuable tool in practice via the R-package R-INLA. In this paper we formalize new developments in the R-INLA package and show how these features greatly extend the scope of models that can be analyzed by this interface. We al...

Bayesian Modelling of Spatial Heterogeneity in Disease

Bayesian Estimation and Prediction of Stochastic Volatility Models via INLA

In this paper we assess Bayesian estimation and prediction using integrated Laplace approximation (INLA) on a stochastic volatility model. This was performed through a Monte Carlo study with 1000 simulated time series. To evaluate the estimation method, two criteria were considered: the bias and square root of the mean square error (smse). The criteria used for prediction are the one step ahead...

Spatial and spatio-temporal models with R-INLA.

During the last three decades, Bayesian methods have developed greatly in the field of epidemiology. Their main challenge focusses around computation, but the advent of Markov Chain Monte Carlo methods (MCMC) and in particular of the WinBUGS software has opened the doors of Bayesian modelling to the wide research community. However model complexity and database dimension still remain a constrai...